Cross Validation in Stochastic Analytic Continuation

TL;DR

This paper introduces a cross validation approach to select the most probable spectral function in stochastic analytic continuation, improving the reliability of spectral features extracted from Quantum Monte Carlo data.

Contribution

It applies cross validation from machine learning to spectral function selection in SAC, addressing the uncertainty in sharp feature identification.

Findings

Effective identification of spectral features using cross validation

Demonstrated method on QMC and synthetic data

Applicable to various analytic continuation techniques

Abstract

Stochastic Analytic Continuation (SAC) of Quantum Monte Carlo (QMC) imaginary-time correlation function data is a valuable tool in connecting many-body models to experimentally measurable dynamic response functions. Recent developments of the SAC method have allowed for spectral functions with sharp features, e.g. narrow peaks and divergent edges, to be resolved with unprecedented fidelity. Often times, it is not known what exact sharp features, if any, are present \textit{a priori}, and, due to the ill-posed nature of the analytic continuation problem, multiple spectral representations may be acceptable. In this work, we borrow from the machine learning and statistics literature and implement a cross validation technique to provide an unbiased method to identify the most likely spectrum amongst a set obtained with different spectral parameterizations and imposed constraints. We demonstrate the power of this method with examples using imaginary-time data generated by QMC simulations and synthetic data generated from artificial spectra. Our procedure, which can be considered a form of model selection, can be applied to a variety of numerical analytic continuation methods, beyond just SAC.